FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction

Yuan, Xiaoming; Chen, Jiahui; Yang, Jiayu; Zhang, Ning; Yang, Tingting; Han, Tao; Taherkordi, Amir

doi:10.1109/tits.2022.3157056

Cited by 44 publications

(25 citation statements)

References 38 publications

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“…In [ 118 ], the authors proposed an edge computing-based graph representation learning approach for short and long traffic flow prediction. The authors used a federated learning approach.…”

Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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“…In [ 118 ], the authors proposed an edge computing-based graph representation learning approach for short and long traffic flow prediction. The authors used a federated learning approach.…”

Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Several authors have proposed FL approaches to deal with distributed graph data. Decentralized graph data widely exist in multiple applications such as traffic flow prediction (Yuan et al, 2022), graph‐level clustering (Caldarola et al, 2021), and node classification (Mei et al, 2019). A knowledge graph could contain text, images, and other types of data, such as multimodal knowledge graphs (M. Chen et al, 2021).…”

Section: Fl Applicationsmentioning

confidence: 99%

Towards federated learning: An overview of methods and applications

Silva

Vinagre

Gama

2023

WIREs Data Min & Knowl

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Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an indepth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.

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“…The VFL has a wide range of applications in intelligent transportation and personalized recommendation [23]. For example, The work in [24] used VFL to train the traffic flow prediction model, since the participants' traffic flow datasets have the same sample space and different spatial characteristics, and VFL can share parameters while ensuring privacy. In [25], a cloudlet-based recommender model was proposed for electric vehicles to find the most relevant charging station, and the model utilizes the VFL technique so that the data does not leave the local.…”

Section: Related Workmentioning

confidence: 99%

Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

Zhang¹,

Wu²,

Li³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The data in Mobile Edge Computing (MEC) contains tremendous market value, and data sharing can maximize the usefulness of the data. However, certain data is quite sensitive, and sharing it directly may violate privacy. Vertical Federated Learning (VFL) is a secure distributed machine learning framework that completes joint model training by passing encrypted model parameters rather than raw data, so there is no data privacy leakage during the training process. Therefore, the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy. Typically, the VFL requires a third party for key distribution and decryption of training results. In this article, we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in MEC. More specifically, we propose a V-Raft consensus algorithm based on Verifiable Random Functions (VRFs), which is a variant of the Raft. The V-Raft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by VFL. Moreover, we apply secret sharing to distribute the private key to avoid the situation where the training result cannot be decrypted if the leader crashes. Finally, we analyzed the performance of the V-Raft and carried out simulation experiments, and the results show that compared with Raft, the V-Raft has higher efficiency and better scalability.

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FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction

Cited by 44 publications

References 38 publications

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Towards federated learning: An overview of methods and applications

Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

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